Insights

Susa Ventures' investment strategy

Written by Seth Berman | January 28, 2022

Seth Berman, co-founder of Susa Ventures, discusses his firms investment philosophy.

Key Takeaways

  • Seth Berman, co-founder and general partner of Susa Ventures, discusses how they continue to pick top winners in the early stage venture capital space.
  • Susa Ventures looks to invest in compounding 'moats' – companies that become more defensible over time. To Susa, these exist primarily in logistics, share tech, fintech, and digital health.
  • They have a robust network of former founders and partners embedded within top technology firms to source deals early and well.
  • Team is by far the most important thing they look for in early companies - which is how they got in early to Robinhood.

Transcript

Hi, I'm Seth Berman. I'm one of the co-founders and general partners of Susa Ventures. I started Angel Investing in 2010, and I was one of the first people that started going to Y Combinator demo days. And I invested in about 30 companies and seven of those companies sold within two years and decided I wanted to start my own fund. And that was kind of the genesis behind Susa. 

Susa’s investment philosophy is investing in compounding moats. So, companies where they become more defensible over time. We really want to invest in companies that are building long term defensibility through whatever they decide to build, whether it's data moat, economies of scale or unique, proprietary data.

And you can kind of think about the four areas that we invest in as logistics and share tech, fintech and digital health. We will do about 20% outside of those theses, but 80% usually fit within one of those buckets. We invest primarily at the early stage. So we try and be the first institutional capital that goes into these companies. Usually it's at pre-seed or seed or right between a one to 3 million dollar check and try and own between ten to 20% of the company.

Susa’s a really incredible fund because we're focused on the community of founders, and we feel like there's a network effect by having more founders on our platform and learning from each other. Building a company is kind of like raising a child where it takes a village to raise a child. It's the people that are building the company that make it successful. And so for us, we really want to have our founders learn from each other. It's almost like an exclusive club, like SoHo House. 

When we first started the fund, we would get a lot of the deals from accelerators, but now it's become so competitive and the funds have become so large that everybody wants their ownership stake at the very beginning. So we have to go directly to the entrepreneurs and how we do that as we have scouts at various companies. It might be Robin Hood or Stripe or Facebook, Google, etc. where they tell us if somebody is leaving the company and they're going to start a new business, then we try and develop a relationship directly with the founder and try to get to the company before any other investor does.

An area that we're really excited about is computer vision, which is basically where computers are able to figure out objects. So if you think about radiologists, they're basically just reading these scans and we invest in a company called VIZ.AI. What they do is read CT scans for strokes. As most people know, if somebody does have a stroke, you want to make sure that you get to them within 90 minutes of the stroke actually happening. So what this company does is it uses computer vision to read the CT scan instantaneously so it knows of that person has that blood clot. This company has defensibility because what they were able to do is take millions of scans that are out there today and ingest them, use that as training data so that they can identify where these occlusions are and then make better decisions for medical care when people go into the hospital. And so we're really excited about that company. As they do this more and more, it gets more accurate. And so the more data it ingests, the more powerful the engine becomes. 

And so our whole thesis was data was the new oil. And any company that had unique data sets, data network effects or created a lot of data through economies of scale would be really interesting companies to invest in.